1. Field of the Invention
The present invention relates to a noise adaptation system of speech model, a noise adaptation method, and a noise adaptation program for speech recognition. In particular, the present invention relates to a noise adaptation system of speech model, a noise adaptation method, and a noise adaptation program for speech recognition that use noisy speech to be recognized to adapt a clean speech model generated by modeling features of speech by means of a Hidden Markov Model (HMM) so that the recognition rate for the noisy environment can be improved.
2. Description of the Related Art
A tree-structure piecewise linear transformation approach is described in the following Non-Patent Document 1. According to the approach described in the document, noise is clustered, a tree-structure noisy speech model space is generated based on the result of the clustering, a speech feature parameter of input noisy speech to be recognized is extracted, an optimum model is selected from the tree-structure noisy speech model space, and linear transformation is applied to the selected model so as to increase the likelihood of the selected model, thereby improving the accuracy for input speech.
(Non-Patent Document 1)
“Effects of tree-structure clustering in noise adaptation using piecewise linear transformation” by Zhipeng Zhang et al. (2002 Autumn Meeting of the Acoustical Society of Japan, pp. 29-30)
In the process of generating a noise-added speech model in the document cited above, only noise data is clustered and then noise is added to the speech, and the noise-added speech model is learned. This approach has a problem that discrepancies arise between features of the noise in the clustering process and features of the noise-added speech model in the model learning process.